{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_moons,make_circles,make_classification\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11b0c9c10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x,y=make_classification(n_samples=100\n",
    "                                ,n_features=2\n",
    "                                ,n_redundant=0\n",
    "                                ,n_informative=2\n",
    "                                ,random_state=1\n",
    "                                ,n_clusters_per_class=1)\n",
    "plt.scatter(x[:,0],x[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11b0a4310>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng=np.random.RandomState(2)\n",
    "x += 2*rng.uniform(size=x.shape)\n",
    "linearly_separable=(x,y)\n",
    "plt.scatter(x[:,0],x[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets = [make_moons(noise=0.3,random_state=0),make_circles(noise=0.2,factor=0.5,random_state=1),linearly_separable]\n",
    "type(datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "figure = plt.figure(figsize=(6,9))\n",
    "i=1\n",
    "for ds_index,ds in enumerate(datasets):\n",
    "    x,y=ds\n",
    "    x=StandardScaler().fit_transform(x)\n",
    "    x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=.4,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
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